Abstract：
BACKGROUND: Cardiac positron emission tomography (PET) can visualize and quantify the molecular and physiological pathways of cardiac function. However, cardiac and respiratory motion can introduce blurring that reduces PET image quality and quantitative accuracy. Dual cardiac- and respiratory-gated PET reconstruction can mitigate motion artifacts but increases noise as only a subset of data are used for each time frame of the cardiac cycle.
OBJECTIVE: The objective of this study is to create a zero-shot image denoising framework using a conditional generative adversarial networks (cGANs) for improving image quality and quantitative accuracy in non-gated and dual-gated cardiac PET images.
METHODS: Our study included retrospective list-mode data from 40 patients who underwent an 18F-fluorodeoxyglucose (18F-FDG) cardiac PET study. We initially trained and evaluated a 3D cGAN-known as Pix2Pix-on simulated non-gated low-count PET data paired with corresponding full-count target data, and then deployed the model on an unseen test set acquired on the same PET/CT system including both non-gated and dual-gated PET data.
RESULTS: Quantitative analysis demonstrated that the 3D Pix2Pix network architecture achieved significantly (p value<0.05) enhanced image quality and accuracy in both non-gated and gated cardiac PET images. At 5%, 10%, and 15% preserved count statistics, the model increased peak signal-to-noise ratio (PSNR) by 33.7%, 21.2%, and 15.5%, structural similarity index (SSIM) by 7.1%, 3.3%, and 2.2%, and reduced mean absolute error (MAE) by 61.4%, 54.3%, and 49.7%, respectively. When tested on dual-gated PET data, the model consistently reduced noise, irrespective of cardiac/respiratory motion phases, while maintaining image resolution and accuracy. Significant improvements were observed across all gates, including a 34.7% increase in PSNR, a 7.8% improvement in SSIM, and a 60.3% reduction in MAE.
CONCLUSIONS: The findings of this study indicate that dual-gated cardiac PET images, which often have post-reconstruction artifacts potentially affecting diagnostic performance, can be effectively improved using a generative pre-trained denoising network.
OBJECTIVE: The objective of this study is to create a zero-shot image denoising framework using a conditional generative adversarial networks (cGANs) for improving image quality and quantitative accuracy in non-gated and dual-gated cardiac PET images.
METHODS: Our study included retrospective list-mode data from 40 patients who underwent an 18F-fluorodeoxyglucose (18F-FDG) cardiac PET study. We initially trained and evaluated a 3D cGAN-known as Pix2Pix-on simulated non-gated low-count PET data paired with corresponding full-count target data, and then deployed the model on an unseen test set acquired on the same PET/CT system including both non-gated and dual-gated PET data.
RESULTS: Quantitative analysis demonstrated that the 3D Pix2Pix network architecture achieved significantly (p value<0.05) enhanced image quality and accuracy in both non-gated and gated cardiac PET images. At 5%, 10%, and 15% preserved count statistics, the model increased peak signal-to-noise ratio (PSNR) by 33.7%, 21.2%, and 15.5%, structural similarity index (SSIM) by 7.1%, 3.3%, and 2.2%, and reduced mean absolute error (MAE) by 61.4%, 54.3%, and 49.7%, respectively. When tested on dual-gated PET data, the model consistently reduced noise, irrespective of cardiac/respiratory motion phases, while maintaining image resolution and accuracy. Significant improvements were observed across all gates, including a 34.7% increase in PSNR, a 7.8% improvement in SSIM, and a 60.3% reduction in MAE.
CONCLUSIONS: The findings of this study indicate that dual-gated cardiac PET images, which often have post-reconstruction artifacts potentially affecting diagnostic performance, can be effectively improved using a generative pre-trained denoising network.
摘要：
背景：心脏正电子发射断层扫描（PET）可以可视化和量化心脏功能的分子和生理途径。然而,心脏和呼吸运动会引入模糊，从而降低PET图像质量和定量精度。双心脏和呼吸门控PET重建可以减轻运动伪影,但增加噪声,因为仅数据的子集用于心动周期的每个时间帧。
目的：本研究的目的是使用条件生成对抗网络（cGAN）创建零拍摄图像去噪框架，以提高非门控和双门控心脏PET图像的图像质量和定量准确性。
方法：我们的研究包括40例接受18F-氟代脱氧葡萄糖（18F-FDG）心脏PET研究的患者的回顾性列表模式数据。我们最初训练并评估了3DcGAN，称为Pix2Pix-on模拟的非门控低计数PET数据与相应的全计数目标数据配对，然后将模型部署在同一PET/CT系统上采集的未知测试集上，包括非门控和双门控PET数据。
结果：定量分析表明，3DPix2Pix网络架构在非门控和门控心脏PET图像中均实现了显着（p值<0.05）增强的图像质量和准确性。5%，10%,和15%保留的计数统计数据，该模型将峰值信噪比(PSNR)提高了33.7%，21.2%,和15.5%，结构相似性指数(SSIM)下降7.1%，3.3%,和2.2%，平均绝对误差(MAE)减少61.4%，54.3%,49.7%，分别。当在双门PET数据上测试时，该模型持续降低了噪音，不考虑心脏/呼吸运动阶段，同时保持图像分辨率和准确性。在所有大门上都观察到了显著的改善，包括PSNR增加34.7%，SSIM改善7.8%，MAE减少60.3%。
结论：这项研究的结果表明，双门控心脏PET图像，通常具有可能影响诊断性能的重建后伪影，可以使用生成预训练去噪网络有效地改进。
目的：本研究的目的是使用条件生成对抗网络（cGAN）创建零拍摄图像去噪框架，以提高非门控和双门控心脏PET图像的图像质量和定量准确性。
方法：我们的研究包括40例接受18F-氟代脱氧葡萄糖（18F-FDG）心脏PET研究的患者的回顾性列表模式数据。我们最初训练并评估了3DcGAN，称为Pix2Pix-on模拟的非门控低计数PET数据与相应的全计数目标数据配对，然后将模型部署在同一PET/CT系统上采集的未知测试集上，包括非门控和双门控PET数据。
结果：定量分析表明，3DPix2Pix网络架构在非门控和门控心脏PET图像中均实现了显着（p值<0.05）增强的图像质量和准确性。5%，10%,和15%保留的计数统计数据，该模型将峰值信噪比(PSNR)提高了33.7%，21.2%,和15.5%，结构相似性指数(SSIM)下降7.1%，3.3%,和2.2%，平均绝对误差(MAE)减少61.4%，54.3%,49.7%，分别。当在双门PET数据上测试时，该模型持续降低了噪音，不考虑心脏/呼吸运动阶段，同时保持图像分辨率和准确性。在所有大门上都观察到了显著的改善，包括PSNR增加34.7%，SSIM改善7.8%，MAE减少60.3%。
结论：这项研究的结果表明，双门控心脏PET图像，通常具有可能影响诊断性能的重建后伪影，可以使用生成预训练去噪网络有效地改进。
